Systems and methods for remotely controlling locomotives with gestures

ABSTRACT

Exemplary embodiments are disclosed of systems and methods for remotely controlling locomotives with gestures. In an exemplary embodiment, a system is configured for allowing an operator(s) to remotely control operation of a locomotive with gesture(s) made by an operator(s). The system includes at least one processor configured to be operable for visually recognizing gesture(s) made by an operator(s) in one or more images captured by at least one camera. A locomotive control unit is configured to be operable for controlling the operation of the locomotive according to the visually recognized gesture(s) made by the operator(s).

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to and the benefit of U.S. ProvisionalPatent Application No. 63/273,893 filed Oct. 30, 2021. The entiredisclosure of U.S. Provisional Patent Application No. 63/273,893 isincorporated herein by reference.

FIELD

The present disclosure generally relates to systems and methods forremotely controlling locomotives with gestures.

BACKGROUND

This section provides background information related to the presentdisclosure which is not necessarily prior art.

A locomotive may include an onboard locomotive control unit (LCU)configured to control one or more aspects of the locomotive, includingstarting, stopping, speed, braking, switching, etc. An operator may usea portable operator control unit (OCU) to remotely control thelocomotive by sending commands, instructions, etc. from the OCU to theLCU via a wireless network. For example, a ground based operator may usethe OCU to remotely control a locomotive in a railyard or a switchingyard.

DRAWINGS

The drawings described herein are for illustrative purposes only ofselected embodiments and not all possible implementations, and are notintended to limit the scope of the present disclosure.

FIG. 1 is a diagram of a system configured to allow an operator toremotely control operation of a locomotive with gestures (e.g., anoperator's hand signal(s), body language, pose(s), etc.) according to anexample embodiment of the present disclosure.

FIG. 2 is a block diagram showing example components including an edgeprocessing device running a neural network that may be included in thesystem shown in FIG. 1 according to an example embodiment of the presentdisclosure.

FIG. 3 is a flow chart illustrating an example method for remotelycontrolling operation of a locomotive with gestures (e.g., an operator'shand signal(s), body language, pose(s), etc.) according to an exampleembodiment of the present disclosure.

Corresponding reference numerals may indicate corresponding (though notnecessarily identical) parts throughout the several views of thedrawings.

DETAILED DESCRIPTION

Example embodiments will now be described more fully with reference tothe accompanying drawings.

Disclosed herein are exemplary systems and methods for remotelycontrolling operation (e.g., start, stop, accelerate, decelerate,lights, audible alerts from bell(s)/horn(s), etc.) of a locomotive withgestures (e.g., hand signal(s), body language, pose(s), etc.) made by anoperator (e.g., a ground based operator in a railyard or switching yard,etc.). As disclosed herein, the exemplary methods and systems utilizecameras and artificial intelligence/visual recognition technology (e.g.,SSD (Single Shot Detector) neural network, YOLO (You Only Look Once)neural network, other neural network, etc.) to visually recognize theoperator's gesture(s).

In exemplary embodiments, a system includes a camera onboard alocomotive. The camera is generally facing (e.g., front-facing, etc.)towards or aligned with (e.g., forward or rearward relative to, etc.)the direction of travel of the locomotive. The camera is configured tocapture image(s) (e.g., still photo(s), video, etc.) of an operator'sgesture(s). For example, a front-facing camera may capture video of theoperator's gesture (e.g., hand signal to increase speed, decrease speed,stop, etc.) as the locomotive passes the operator while the locomotiveis moving along a track. Or, for example, the front-facing camera maycapture video of the operator's gesture(s) (e.g., start moving along thetrack, etc.) while the locomotive is stationary. The system alsoincludes a processor (e.g., a trained visual recognition softwareprogram, component, or module, edge processing device running a neuralnetwork, etc.) that analyzes the video (broadly, image(s)) from thefront-facing camera to visually recognize the operator's gesture(s) inthe video. If the visually recognized operator gesture is determined tosufficiently correspond with a gesture allocated to a locomotivefunction or operation (e.g., accelerate, decelerate, start, stop, etc.),then the function or operation may be implemented or executedaccordingly by a locomotive control unit (LCU) onboard the locomotive.

In a first example, an operator outside of and not onboard a locomotivemay hold up a hand in a “stop” pose. An image(s) (e.g., a stillphoto(s), video, etc.) of the operator's hand signal in the stop posemay be captured by at least one camera onboard the locomotive. Thecamera may be connected to an edge computing device or processing device(e.g., Nividia Jetson edge device, other processing or computing device,etc.) running a neural network (e.g., SSD neural network, YOLO neuralnetwork, other neural network, other artificial intelligence or machinelearning network, etc.). The edge processing device is in communication(e.g., via a serial connection, other connection, etc.) with alocomotive control unit (LCU) onboard the locomotive for relayingcommands, instructions, etc. to the LCU. In this example, the edgeprocessing device running the neural network analyzes the image(s)captured by the camera and visually recognizes the operator's handsignal in the stop pose. If it is determined that the visuallyrecognized operator's hand signal in the stop pose sufficientlycorresponds with a gesture allocated to a stop function, then the edgeprocessing device interacts with the locomotive control unit (LCU) byrelaying a stop command to the LCU over the serial connection. Inresponse to receiving the stop command, the LCU may then control (e.g.,algorithmically control, etc.) the locomotive to stop, e.g., accordingto a stopping trajectory including a deceleration profile, etc.

In a second example, an operator may wave a hand to “wave on” thelocomotive. Video of the operator's hand waving may be captured by thecamera onboard the locomotive. In this example, the edge processingdevice running the neural network analyzes the video captured by thecamera and visually detects or recognizes the operator's hand signalwaving on the locomotive. If it is determined that the visuallyrecognized operator hand waving sufficiently corresponds with a gestureallocated to a move onward function, then the edge processing deviceinteracts with the locomotive control unit (LCU) and relays a command tothe LCU over the serial connection. In response to receiving thecommand, the LCU may then control (e.g., algorithmically control, etc.)the locomotive such that the locomotive and its payload are movedforward (e.g., slowly inched forward, etc.).

Advantageously, the exemplary gesture-based remote control locomotive(RCL) systems and methods disclosed herein may provide the ability tomake small adjustments to a locomotive's speed, stopping, or lights,etc. in a natural and rapid way without any other required input.Exemplary embodiments disclosed herein may provide the ability tocontrol locomotives using natural hand/body signals commonly acceptedwithin the rail industry without a second operator or reliance on anOperator Control Unit (OCU). For example, exemplary embodimentsdisclosed herein may be configured to permit hand or lantern signalsthat were used prior to RCL systems to be used again by an operator forcontrolling a locomotive without the requirement of the operator wearingan OCU.

FIGS. 1 and 2 illustrate an exemplary embodiment of a system 100 forremotely controlling operation of the locomotive with gesture(s) made byan operator(s) according to some aspects of the present disclosure. Thelocomotive 104 generally includes a tractive effort mechanism for movingthe locomotive 104 along a track 108, and a braking mechanism forreducing a speed of the locomotive 104 along the track 108.

A locomotive controller or control unit (LCU) 112 is located onboard thelocomotive 104. At least one camera 116 is also onboard the locomotive104. The at least one camera 116 is generally facing forward relative toa direction of travel of the locomotive 104 for capturing image(s)(e.g., still photos, video, etc.) of gesture(s) made by the operator120. For example, the camera 116 may capture video of the operator'sgesture (e.g., hand signal to increase speed, decrease speed, stop,etc.) as the locomotive 104 passes the operator 120 while the locomotive104 is moving along a track 108. Or, for example, the camera 116 maycapture video of the operator's gesture(s) (e.g., start moving along thetrack, etc.) while the locomotive 104 is stationary and stopped alongthe track 108.

As shown in FIG. 2 , this exemplary system 100 includes an edgeprocessing device 124 running a neural network (e.g., SSD (Single ShotDetector) neural network, YOLO (You Only Look Once) neural network,other neural network, etc.). The edge processing device 124 is incommunication with the camera 116 via a communication link 128 overwhich the edge processing device 124 may receive image(s) of theoperator 120 captured by the camera 116. The edge processing device 124is also in communication with the LCU 112 via communication link 132(e.g., a serial connection, etc.) over which the edge processing device124 interacts with and relays information (e.g., decisions, commands,instructions, etc.) to the LCU 112.

The edge processing device 124 is configured to be operable fordetecting and recognizing gesture(s) (e.g., natural yet specific bodylanguage, etc.) of the operator 120 in the images captured by the camera116. In this exemplary embodiment, the edge processing device 124interacts with the LCU 112 onboard the locomotive 104 over the serialconnection 132 to relay decision(s) based on visual observation(s)determined through the neural network model. The LCU 112 may thenprecisely control the locomotive 104 in accordance with an algorithm(s)associated with or allocated to the visually recognized operator'sgesture(s). By way of example, the system 100 may include an SSD neuralnetwork that is trained (e.g., on cloud servers, etc.) and then deployedon the edge processing device 124 to detect industry standard operatorcommands from a two man crew, etc.

With continued reference to FIG. 2 , the LCU 112 includes a memory 136to store computer-executable instructions (e.g., algorithm(s), etc.) anda processor 140 in communication with the memory 136 to execute thecomputer-executable instructions within the memory 136.

In this illustrated exemplary embodiment, the LCU 112 is incommunication via link 132 with the edge processing device 124, which,in turn, is in communication via link 128 with the camera 116 (FIG. 1 ).In alternative embodiments, the LCU 112 may integrally include orcomprise the artificial intelligence/visual recognition technology forvisually recognizing the operator's gesture(s). In which case, the LCU112 may receive a video feed of the operator 120 captured by the camera116. The processor 140 of the LCU 112 may be configured for analyzingthe video feed from the camera 116 to visually recognize or identify thegesture(s) made by the operator 120. The locomotive controller 112 maythen control (e.g., algorithmically control, etc.) the locomotive 104according to the function or operation associated with the operator'sgesture(s) as visually recognized by the processor 140 of the locomotivecontroller 112. In this alternative embodiment, the locomotivecontroller 112 may therefore directly control the locomotive 104 basedon the operator's gesture(s) as visually recognized by the processor 140within the locomotive controller 112, e.g., without the locomotivecontroller 112 having to receive command(s) or instruction(s) relayedfrom the edge processing device 124, etc.

The locomotive controller 112 may further include one or more wirelessinterfaces 144 (e.g., data ports, etc.), such as a short-range wirelesscommunication interface, a Wi-Fi wireless communication interface, acellular communication interface, other radio frequency (RF) interfaces,etc. The locomotive controller 112 may also include a global navigationsatellite system (GNSS) antenna 148 (e.g., a GPS antenna, etc.), one ormore accelerometers (e.g., an accelerometer array, a singleaccelerometer, etc.), etc. The locomotive controller 112 may beconfigured to report location, one or more parameters, etc. to anoperator control unit, yard control server, etc.

The locomotive controller 112 may include a display 152 and an input156. The display 152 can be any suitable display (e.g., a liquid crystaldisplay (LCD), light emitting diodes (LED), indicator lights, etc.). Theinput 156 can include any suitable input element(s) (e.g., a keypad,touchscreen, switches, etc.), for receiving inputs (e.g., commands,etc.) from an operator.

In exemplary embodiments, the locomotive controller 112 may also beconfigured for communication with an operator control unit (OCU) forreceiving commands from the operator control unit. In such exemplaryembodiments, the locomotive 104 may be remotely controlled by thelocomotive controller 112 via the operator's gestures and via commandsreceived from the operator control unit. The operator control unit mayinclude a user interface for receiving input from an operator and awireless interface in communication with the locomotive controller 112.The operator control unit may be configured to receive one or morecontrol commands from the operator via the user interface, and theoperator control unit may be configured to transmit the received one ormore control commands to the locomotive controller 112 to controloperation of the locomotive 104. The operator control unit may includean enclosure (e.g., a housing, etc.) including a user interface, adisplay, etc. The operator control unit may include a processor,battery, memory, a global navigation satellite system (GNSS) antenna(e.g., a GPS antenna, etc.), one or more accelerometers (e.g., anaccelerometer array, a single accelerometer, etc.) for tilt detection,etc.

The operator control unit may include a wireless interface forcommunicating with the locomotive controller 112 via an RF channel, etc.The operator control unit may include an optional global navigationsatellite system (GNSS) antenna for determining a location of theoperator control unit. For example, the GNSS antenna may be a globalpositioning system (GPS) antenna. The operator control unit may includea tilt sensor (e.g., an accelerometer array, a single accelerometer,etc.) for determining a tilt condition (e.g., a fall event of a fieldoperator, etc.). The operator control unit may include an enclosure(e.g., a housing, etc.) including the user interface, the display, etc.

In exemplary embodiments, the system 100 may also be configured todetermine whether or not the operator 120 is facing the locomotive 104from the visually recognized images of the operator's gesture(s). Forexample, if the system 100 determines that the operator 120 is facingthe locomotive 104, then the system 100 may determine that thegesture(s) of the operator 120 facing the locomotive 104 is applicableand therefore usable for remotely controlling the locomotive 104.Conversely, if the system 100 determines that the operator 120 is notfacing the locomotive 104, then the system 100 may determine that thegesture(s) of the operator 120 that is not facing the locomotive 104 isnot applicable and therefore not usable for remotely controlling thelocomotive 104.

In exemplary embodiments, the system 100 may be configured to determinewhether or not the operator 120 is on the port (left) or starboard(right) side of the locomotive 104 by monitoring the direction that theoperator 120 enters and exits the frame of vision as the locomotive 104passes the operator 120. For example, if the system 100 determines thatthe operator 120 is on the starboard side of the locomotive 104, thenthe system 100 may determine that the starboard side operator'sgesture(s) is applicable and therefore usable for remotely controllingthe locomotive 104. Conversely, if the system 100 determines that theoperator 120 is on the port side of the locomotive 104, then the system100 may determine that the port side operator's gesture(s) is notapplicable and therefore not usable for remotely controlling thelocomotive 104.

In FIG. 1 , the camera 116 is mounted forward facing on the front of thelocomotive 104 for capturing images of the operator 120. In otherembodiments, the camera 116 may be located in any other suitablelocation for capturing images of the operator 120.

Although FIG. 1 illustrates a single locomotive 104, the locomotive 104may be part of a locomotive consist that includes one or morelocomotives, rail cars, etc. coupled to the locomotive 104. Thelocomotives of the consist may operate in tandem (e.g., by remotecontrol, etc.), and may require electrical and pneumatic connections inorder to operate together. The locomotive controller 112 may beconfigured to control movement of the locomotive consist along the track108 (e.g., via a tractive effort mechanism, via a pneumatic brakingsystem, etc.). Similarly, although FIG. 1 illustrates a single train car160, other embodiments may include a train having more than one traincar 160 coupled to the locomotive 104, no train cars 160 coupled to thelocomotive 104, etc.

FIG. 3 illustrates an example a method 302 for remotely controllingoperation of a locomotive with gestures (e.g., an operator's handsignal(s), body language, pose(s), etc.) according to an exampleembodiment of the present disclosure.

As shown in FIG. 3 , the method 302 includes, at 306, using a camera(s)to capture image(s) (e.g., still photos, video, etc.) of gesture(s) madeby an operator(s).

At 310, the method 302 includes using an edge processing device(broadly, a processor) running a neural network to visually recognizethe operator's gesture(s) in the image(s) captured by the camera(s).

At 314, the method 302 includes relaying decision(s), command(s),instruction(s), etc. to a locomotive control unit (LCU) onboard thelocomotive based on the visual observations or visually recognizedoperator gestures(s) determined through the neural network model.

At 318, the method 302 includes the LCU controlling operation of thelocomotive according to the received decision(s), command(s),instruction(s), etc. For example the LCU may control the locomotive inaccordance with an algorithm(s) associated with or allocated to thevisually recognized operator's gesture(s).

Exemplary embodiments are disclosed of systems and methods for remotelycontrolling locomotives with gestures. In an exemplary embodiment, asystem is configured for allowing an operator(s) to remotely controloperation of a locomotive with gesture(s) made by an operator(s). Thesystem includes at least one processor configured to be operable forvisually recognizing gesture(s) made by an operator(s) in one or moreimages captured by at least one camera. A locomotive control unit isconfigured to be operable for controlling the operation of thelocomotive according to the visually recognized gesture(s) made by theoperator(s).

In exemplary embodiments, the at least one processor comprises an edgeprocessing device and/or a neural network (e.g., SSD neural network,YOLO neural network, other neural network, etc.). For example, the atleast one processor may comprise an edge processing device running anSSD neural network.

In exemplary embodiments, the at least one processor is in communicationvia a serial connection with the locomotive control unit. The at leastone processor is configured to operable for relaying one or moredecisions, commands, and/or instructions to the locomotive control unitover the serial connection based on the visually recognized gesture(s).The locomotive control unit is configured to be operable for controllingoperation of the locomotive according to the one or more decisions,commands, and/or instructions relayed to the locomotive control unitfrom the at least one processor over the serial connection.

In exemplary embodiments, the locomotive control unit is configured tobe operable for controlling operation of the locomotive in accordancewith an algorithm(s) associated with or allocated to the visuallyrecognized gesture(s).

In exemplary embodiments, the at least one camera, the at least oneprocessor, and the locomotive control unit are onboard the locomotive.

In exemplary embodiments, the system includes the at least one cameracomprising at least one video camera onboard the locomotive forcapturing video of the gesture(s) made by the operator(s). The at leastone processor is configured to visually recognize the gesture(s) made bythe operator(s) in the video captured by the at least one video camera.

In exemplary embodiments, the at least one processor is configured to beoperable for visually recognizing gesture(s) made by an operator(s) thatcomprise one or more of an operator's hand signal(s), body language,and/or pose(s).

Also disclosed are exemplary methods of remotely controlling operationof a locomotive with gesture(s) made by an operator(s). In exemplaryembodiments, the method includes capturing, via at least one camera, oneor more images of gesture(s) made by an operator(s); visuallyrecognizing, via at least one processor, the gesture(s) made by theoperator(s) in the one or more images captured by the at least onecamera; and controlling operation of the locomotive, via a locomotivecontrol unit, according to the visually recognized gesture(s) made bythe operator(s).

In exemplary embodiments, the at least one processor comprises an edgeprocessing device. And the method includes visually recognizing, via theedge processing device, the gesture(s) made by the operator(s) in theone or more images captured by the at least one camera.

In exemplary embodiments, the at least one processor comprises a neuralnetwork. And the method includes visually recognizing, via the neuralnetwork, the gesture(s) made by the operator(s) in the one or moreimages captured by the at least one camera.

In exemplary embodiments, the at least one processor comprises an edgeprocessing device running a single shot detector (SSD) neural network.And the method includes visually recognizing, via the edge processingdevice running the SSD neural network, the gesture(s) made by theoperator(s) in the one or more images captured by the at least onecamera.

In exemplary embodiments, the at least one processor is in communicationvia a serial connection with the locomotive control unit. And the methodincludes relaying one or more decisions, commands, and/or instructionsbased on the visually recognized gesture(s) from the at least oneprocessor over the serial connection to the locomotive control unit. Themethod may further include controlling operation of the locomotive, viathe locomotive control unit, according to the one or more decisions,commands, and/or instructions relayed to the locomotive control unitfrom the at least one processor over the serial connection.

In exemplary embodiments, the method includes controlling operation ofthe locomotive, via the locomotive control unit, in accordance with analgorithm(s) associated with or allocated to the visually recognizedgesture(s).

In exemplary embodiments, the at least one camera, the at least oneprocessor, and the locomotive control unit are onboard the locomotive.

In exemplary embodiments, the at least one camera comprising at leastone video camera onboard the locomotive for capturing video of thegesture(s) made by the operator(s). And the method includes: capturing,via the at least one video camera, video of the gesture(s) made by theoperator(s); and visually recognizing, via the at least one processor,the gesture(s) made by the operator(s) in the video captured by the atleast one video camera.

In exemplary embodiments, the visually recognized gesture(s) made by theoperator(s) comprise one or more of an operator's hand signal(s), bodylanguage, and/or pose(s).

In exemplary embodiments, the exemplary systems and methods disclosedherein may be configured to allow an operator(s) to remotely controloperation of a locomotive by using standard railroad manual signals. Insuch exemplary embodiments, the exemplary systems and methods may beconfigured to capture, via at least one camera, one or more images ofthe signal(s) made by an operator(s), visually recognize, via at leastone processor, the signal(s) made by the operator(s) in the one or moreimages captured by the at least one camera, and control operation of thelocomotive, via a locomotive control unit, according to the visuallyrecognized signal(s) made by the operator(s).

The manual signals may be given with the operator's hand, a flag, or alantern or flashlight (broadly, a light source) during the dailyperformance of the railroader/operator's work. The manual signals mayinclude Train Has Parted, Reduce Speed, Easy, Stop, Back Up, Release AirBrakes, Apply Air Brakes, Whistle, and Proceed.

For the Train Has Parted signal, the operator swings a flag, a lightsource, or hand vertically in a full circle at full arm's length acrossthe track when the train is running.

For the Reduce Speed signal, the operator holds a flag, a light source,or hand horizontally at arm's length.

For the Easy signal, the operator horizontally moves a flag, a lightsource, or hand back and forth from “A” to “B” at a slow or fast paceaccording to the speed of movement desired. Generally, location A is tothe side of the operator's at full arm's length, and location B is incloser to or in front of the operator's shoulder.

For the Stop signal, the operator swings a flag, a light source, or handback and forth across the track.

For the Back Up signal, the operator swings a flag, a light source, orhand vertically in a circle at half arm's length across the track whenthe train is standing or backing.

For the Release Air Brakes signal, the operator holds a flag, a lightsource, or hand at arm's length above the head when the train isstanding.

For the Apply Air Brakes signal, the operator swings a flag, a lightsource, or hand horizontally above the head when the train is standing.

For the Whistle signal, the operator vertically swings a flag, a lightsource, or hand between “A” and “B” to simulate pulling a whistle cord.Generally, location A is above the operator's head, and location B isabove the operator's shoulder but below the location A.

For the Proceed signal, the operator raises and lowers a flag, a lightsource, or hand vertically.

Although various exemplary embodiments are described herein in relationto remotely controlling locomotives with operator gesture(s), thesystems and methods disclosed herein are applicable to other industrialmachines and machine control units. Alternative embodiments arecontemplated in relation to various other types of machines, includingoverhead crane systems, mobile cranes, other industrial machines, othermachine control units in addition to locomotive control units, etc.

In exemplary embodiments, the exemplary systems and methods disclosedherein may be configured to allow an operator(s) to remotely controloperation of a mobile crane by using mobile crane hand signals. In suchexemplary embodiments, the exemplary systems and methods may beconfigured to capture, via at least one camera, one or more images ofthe signal(s) made by an operator(s), visually recognize, via at leastone processor (e.g., an edge processing device running a neural network,etc.), the signal(s) made by the operator(s) in the one or more imagescaptured by the at least one camera, and control operation of the mobilecrane, via a machine control unit (MCU), according to the visuallyrecognized signal(s) made by the operator(s). The mobile crane handsignals may include Hoist, Lower, Use Main Hoist, Use Whipline, RaiseBoom, Lower Boom, Move Slowly, Raise the Boom and Lower the Load, Lowerthe Boom and Raise Load, Swing, Stop, Emergency Stop, Travel, DogEverything, Travel (Both Tracks), Travel (One Track), Extend Boom,Retract Boom, Extend Boom (One Hand), and Retract Boom (One Hand).

Example embodiments are provided so that this disclosure will bethorough, and will fully convey the scope to those who are skilled inthe art. Numerous specific details are set forth such as examples ofspecific components, devices, and methods, to provide a thoroughunderstanding of embodiments of the present disclosure. It will beapparent to those skilled in the art that specific details need not beemployed, that example embodiments may be embodied in many differentforms, and that neither should be construed to limit the scope of thedisclosure. In some example embodiments, well-known processes,well-known device structures, and well-known technologies are notdescribed in detail. In addition, advantages and improvements that maybe achieved with one or more exemplary embodiments of the presentdisclosure are provided for purposes of illustration only and do notlimit the scope of the present disclosure, as exemplary embodimentsdisclosed herein may provide all or none of the above mentionedadvantages and improvements and still fall within the scope of thepresent disclosure.

Specific dimensions, specific materials, and/or specific shapesdisclosed herein are example in nature and do not limit the scope of thepresent disclosure. The disclosure herein of particular values andparticular ranges of values for given parameters are not exclusive ofother values and ranges of values that may be useful in one or more ofthe examples disclosed herein. Moreover, it is envisioned that any twoparticular values for a specific parameter stated herein may define theendpoints of a range of values that may be suitable for the givenparameter (i.e., the disclosure of a first value and a second value fora given parameter can be interpreted as disclosing that any valuebetween the first and second values could also be employed for the givenparameter). For example, if Parameter X is exemplified herein to havevalue A and also exemplified to have value Z, it is envisioned thatparameter X may have a range of values from about A to about Z.Similarly, it is envisioned that disclosure of two or more ranges ofvalues for a parameter (whether such ranges are nested, overlapping ordistinct) subsume all possible combination of ranges for the value thatmight be claimed using endpoints of the disclosed ranges. For example,if parameter X is exemplified herein to have values in the range of1-10, or 2-9, or 3-8, it is also envisioned that Parameter X may haveother ranges of values including 1-9, 1-8, 1-3, 1-2, 210, 2-8, 2-3,3-10,and 3-9.

The term “about” when applied to values indicates that the calculationor the measurement allows some slight imprecision in the value (withsome approach to exactness in the value; approximately or reasonablyclose to the value; nearly). If, for some reason, the imprecisionprovided by “about” is not otherwise understood in the art with thisordinary meaning, then “about” as used herein indicates at leastvariations that may arise from ordinary methods of measuring or usingsuch parameters. For example, the terms “generally”, “about”, and“substantially” may be used herein to mean within manufacturingtolerances.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. Forexample, when permissive phrases, such as “may comprise”, “may include”,and the like, are used herein, at least one embodiment comprises orincludes the feature(s). As used herein, the singular forms “a,” “an,”and “the” may be intended to include the plural forms as well, unlessthe context clearly indicates otherwise. The terms “comprises,”“comprising,” “including,” and “having,” are inclusive and thereforespecify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. The method steps,processes, and operations described herein are not to be construed asnecessarily requiring their performance in the particular orderdiscussed or illustrated, unless specifically identified as an order ofperformance. It is also to be understood that additional or alternativesteps may be employed.

When an element or layer is referred to as being “on,” “engaged to,”“connected to,” or “coupled to” another element or layer, it may bedirectly on, engaged, connected or coupled to the other element orlayer, or intervening elements or layers may be present. In contrast,when an element is referred to as being “directly on,” “directly engagedto,” “directly connected to,” or “directly coupled to” another elementor layer, there may be no intervening elements or layers present. Otherwords used to describe the relationship between elements should beinterpreted in a like fashion (e.g., “between” versus “directlybetween,” “adjacent” versus “directly adjacent,” etc.). As used herein,the term “and/or” includes any and all combinations of one or more ofthe associated listed items.

Although the terms first, second, third, etc. may be used herein todescribe various elements, components, regions, layers and/or sections,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer or section from another region,layer or section. Terms such as “first,” “second,” and other numericalterms when used herein do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer or section discussed below could be termed a second element,component, region, layer or section without departing from the teachingsof the example embodiments.

The foregoing description of the embodiments has been provided forpurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure. Individual elements, intended orstated uses, or features of a particular embodiment are generally notlimited to that particular embodiment, but, where applicable, areinterchangeable and can be used in a selected embodiment, even if notspecifically shown or described. The same may also be varied in manyways. Such variations are not to be regarded as a departure from thedisclosure, and all such modifications are intended to be includedwithin the scope of the disclosure.

What is claimed is:
 1. A system configured for allowing an operator(s)to remotely control operation of a locomotive with gesture(s) made by anoperator(s), the system comprising: at least one processor configured tobe operable for visually recognizing gesture(s) made by an operator(s)in one or more images captured by at least one camera; and a locomotivecontrol unit configured to be operable for controlling the operation ofthe locomotive according to the visually recognized gesture(s) made bythe operator(s).
 2. The system of claim 1, wherein the at least oneprocessor comprises an edge processing device.
 3. The system of claim 1,wherein the at least one processor comprises a neural network.
 4. Thesystem of claim 1, wherein the at least one processor comprises an edgeprocessing device running a single shot detector (SSD) neural network.5. The system of claim 1, wherein: the at least one processor is incommunication via a serial connection with the locomotive control unit;and the at least one processor is configured to be operable for relayingone or more decisions, commands, and/or instructions to the locomotivecontrol unit over the serial connection based on the visually recognizedgesture(s).
 6. The system of claim 5, wherein the locomotive controlunit is configured to be operable for controlling operation of thelocomotive according to the one or more decisions, commands, and/orinstructions relayed to the locomotive control unit from the at leastone processor over the serial connection.
 7. The system of claim 1,wherein the locomotive control unit is configured to be operable forcontrolling operation of the locomotive in accordance with analgorithm(s) associated with or allocated to the visually recognizedgesture(s).
 8. The system of claim 1, wherein the at least one camera,the at least one processor, and the locomotive control unit are onboardthe locomotive.
 9. The system of claim 1, wherein: the system includesthe at least one camera comprising at least one video camera onboard thelocomotive for capturing video of the gesture(s) made by theoperator(s); and the at least one processor is configured to visuallyrecognize the gesture(s) made by the operator(s) in the video capturedby the at least one video camera.
 10. The system of claim 1, wherein theat least one processor is configured to be operable for visuallyrecognizing gesture(s) made by the operator(s) including one or more ofan operator's hand signal(s), body language, and/or pose(s).
 11. Amethod of remotely controlling operation of a locomotive with gesture(s)made by an operator(s), the method comprising: capturing, via at leastone camera, one or more images of gesture(s) made by an operator(s);visually recognizing, via at least one processor, the gesture(s) made bythe operator(s) in the one or more images captured by the at least onecamera; and controlling operation of the locomotive, via a locomotivecontrol unit, according to the visually recognized gesture(s) made bythe operator(s).
 12. The method of claim 11, wherein: the at least oneprocessor comprises an edge processing device; and the method includesvisually recognizing, via the edge processing device, the gesture(s)made by the operator(s) in the one or more images captured by the atleast one camera.
 13. The method of claim 11, wherein: the at least oneprocessor comprises a neural network; and the method includes visuallyrecognizing, via the neural network, the gesture(s) made by theoperator(s) in the one or more images captured by the at least onecamera.
 14. The method of claim 11, wherein: the at least one processorcomprises an edge processing device running a single shot detector (SSD)neural network; and the method includes visually recognizing, via theedge processing device running the single shot detector (SSD) neuralnetwork, the gesture(s) made by the operator(s) in the one or moreimages captured by the at least one camera.
 15. The method of claim 11,wherein: the at least one processor is in communication via a serialconnection with the locomotive control unit; and the method includesrelaying one or more decisions, commands, and/or instructions based onthe visually recognized gesture(s) from the at least one processor overthe serial connection to the locomotive control unit.
 16. The method ofclaim 15, wherein the method includes controlling operation of thelocomotive, via the locomotive control unit, according to the one ormore decisions, commands, and/or instructions relayed to the locomotivecontrol unit from the at least one processor over the serial connection.17. The method of claim 11, wherein the method includes controllingoperation of the locomotive, via the locomotive control unit, inaccordance with an algorithm(s) associated with or allocated to thevisually recognized gesture(s).
 18. The method of claim 11, wherein theat least one camera, the at least one processor, and the locomotivecontrol unit are onboard the locomotive.
 19. The method of claim 11,wherein: the at least one camera comprising at least one video cameraonboard the locomotive for capturing video of the gesture(s) made by theoperator(s); and the method includes: capturing, via the at least onevideo camera, video of the gesture(s) made by the operator(s); andvisually recognizing, via the at least one processor, the gesture(s)made by the operator(s) in the video captured by the at least one videocamera.
 20. The method of claim 11, wherein the visually recognizedgesture(s) made by the operator(s) comprise one or more of an operator'shand signal(s), body language, and/or pose(s).